據說，別人去NIPS 2017是這樣的：
谷歌去NIPS 2017是這樣的：
雷鋒網AI科技評論按：今天，人工智慧領域本年度最後一個學術盛會、機器學習領域頂級會議、第31屆神經信息處理系統大會（NIPS 2017）就要在加州長灘市開啟了。（雷鋒網AI科技評論記者也將親臨現場進行全程報導！）
谷歌作為鑽石贊助商，今年共有450人去參加NIPS大會，而我們知道NIPS 2017的參會人數總共有5000+，所以如果你在會場，那麼放眼望去，看到的每13個人差不多就有一個是谷歌的人，並且人家這些人還都不是來玩的。
一、活動情況
1、接收論文（Accepted Papers）
據雷鋒網了解，今年NIPS會議共有3240篇投稿論文，其中678篇入選（20.9%），40篇orals，112篇spotlights。
在這些入選論文中，國內高校共有19篇論文入選；UC伯克利有16篇，斯坦福有20篇，MIT有20篇，而卡內基·梅隆大學則有高達32篇入選論文。是不是很牛逼？
說真的，並不！
谷歌有45篇入選論文，遠超世界頂級的四大高校，更是遠超太平洋西岸某一大國的所有高校之和。這裡是谷歌入選論文列表：
A Meta-Learning Perspective on Cold-Start Recommendations for ItemsManasi Vartak, Hugo Larochelle, Arvind ThiagarajanAdaGAN: Boosting Generative ModelsIlya Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard SchlkopfDeep Lattice Networks and Partial Monotonic FunctionsSeungil You, David Ding, Kevin Canini, Jan Pfeifer, Maya GuptaFrom which world is your graphCheng Li, Varun Kanade, Felix MF Wong, Zhenming LiuHiding Images in Plain Sight: Deep SteganographyShumeet BalujaImproved Graph Laplacian via Geometric Self-ConsistencyDominique Joncas, Marina Meila, James McQueenModel-Powered Conditional Independence TestRajat Sen, Ananda Theertha Suresh, Karthikeyan Shanmugam, Alexandros Dimakis, Sanjay ShakkottaiNonlinear random matrix theory for deep learningJeffrey Pennington, Pratik WorahResurrecting the sigmoid in deep learning through dynamical isometry: theory and practiceJeffrey Pennington, Samuel Schoenholz, Surya GanguliSGD Learns the Conjugate Kernel Class of the NetworkAmit DanielySVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and InterpretabilityMaithra Raghu, Justin Gilmer, Jason Yosinski, Jascha Sohl-DicksteinLearning Hierarchical Information Flow with Recurrent Neural ModulesDanijar Hafner, Alexander Irpan, James Davidson, Nicolas HeessOnline Learning with Transductive RegretScott Yang, Mehryar MohriAcceleration and Averaging in Stochastic Descent DynamicsWalid Krichene, Peter BartlettParameter-Free Online Learning via Model SelectionDylan J Foster, Satyen Kale, Mehryar Mohri, Karthik SridharanDynamic Routing Between CapsulesSara Sabour, Nicholas Frosst, Geoffrey E HintonModulating early visual processing by languageHarm de Vries, Florian Strub, Jeremie Mary, Hugo Larochelle, Olivier Pietquin, Aaron C CourvilleMarrNet: 3D Shape Reconstruction via 2.5D SketchesJiajun Wu, Yifan Wang, Tianfan Xue, Xingyuan Sun, Bill Freeman, Josh TenenbaumAffinity Clustering: Hierarchical Clustering at ScaleMahsa Derakhshan, Soheil Behnezhad, Mohammadhossein Bateni, Vahab Mirrokni, MohammadTaghi Hajiaghayi, Silvio Lattanzi, Raimondas KiverisAsynchronous Parallel Coordinate Minimization for MAP InferenceOfer Meshi, Alexander SchwingCold-Start Reinforcement Learning with Softmax Policy GradientNan Ding, Radu SoricutFiltering Variational ObjectivesChris J Maddison, Dieterich Lawson, George Tucker, Mohammad Norouzi, Nicolas Heess, Andriy Mnih, Yee Whye Teh, Arnaud DoucetMulti-Armed Bandits with Metric Movement CostsTomer Koren, Roi Livni, Yishay MansourMultiscale Quantization for Fast Similarity SearchXiang Wu, Ruiqi Guo, Ananda Theertha Suresh, Sanjiv Kumar, Daniel Holtmann-Rice, David Simcha, Felix YuReducing Reparameterization Gradient VarianceAndrew Miller, Nicholas Foti, Alexander D'Amour, Ryan AdamsStatistical Cost SharingEric Balkanski, Umar Syed, Sergei VassilvitskiiThe Unreasonable Effectiveness of Structured Random Orthogonal EmbeddingsKrzysztof Choromanski, Mark Rowland, Adrian WellerValue Prediction NetworkJunhyuk Oh, Satinder Singh, Honglak LeeREBAR: Low-variance, unbiased gradient estimates for discrete latent variable modelsGeorge Tucker, Andriy Mnih, Chris J Maddison, Dieterich Lawson, Jascha Sohl-DicksteinApproximation and Convergence Properties of Generative Adversarial LearningShuang Liu, Olivier Bousquet, Kamalika ChaudhuriAttention is All you NeedAshish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, ukasz Kaiser, Illia PolosukhinPASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inferenceJonathan Huggins, Ryan Adams, Tamara BroderickRepeated Inverse Reinforcement LearningKareem Amin, Nan Jiang, Satinder SinghFair Clustering Through FairletsFlavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei VassilvitskiiAffine-Invariant Online Optimization and the Low-rank Experts ProblemTomer Koren, Roi LivniBatch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized ModelsSergey IoffeBridging the Gap Between Value and Policy Based Reinforcement LearningOfir Nachum, Mohammad Norouzi, Kelvin Xu, Dale SchuurmansDiscriminative State Space ModelsVitaly Kuznetsov, Mehryar MohriDynamic Revenue SharingSantiago Balseiro, Max Lin, Vahab Mirrokni, Renato Leme, Song ZuoMulti-view Matrix Factorization for Linear Dynamical System EstimationMahdi Karami, Martha White, Dale Schuurmans, Csaba SzepesvariOn Blackbox Backpropagation and Jacobian SensingKrzysztof Choromanski, Vikas SindhwaniOn the Consistency of Quick ShiftHeinrich JiangRevenue Optimization with Approximate Bid PredictionsAndres Munoz, Sergei VassilvitskiiShape and Material from SoundZhoutong Zhang, Qiujia Li, Zhengjia Huang, Jiajun Wu, Josh Tenenbaum, Bill FreemanLearning to See Physics via Visual De-animationJiajun Wu, Erika Lu, Pushmeet Kohli, Bill Freeman, Josh Tenenbaum
2、Invited talk
NIPS 2017在4-7日期間安排了7場大會報告，其中谷歌作為鑽石贊助商，其首席科學家John Platt將在4日下午5：30-6：20做首場invited talk：《Powering the next 100 years》，來講述谷歌如何使用機器學習來解決未來的能源問題。他是這麼說的：
我的夢想就是讓地球上的每一個人每年都能夠用上和美國普通人一樣多的能源。如果實現這個目標，那麼在2100年，就需要0.2 x 10^24焦耳的能量，這是非常巨大的。那麼人類文明如何能夠獲得這麼多能量而同時不會導致二氧化碳含量劇增呢？為了回答這個問題，我首先要深入到電力經濟學，以了解當前零碳技術的局限性。這些限制也是導致我們仍然在研究如何開發零碳技術（例如核聚變）的原因。對於核聚變，我將說明為什麼發展了近70年，對它的開發仍然是一個棘手的問題，而為什麼在不久的將來又可能會得到一個很好的解決方案。我還將解釋我們如何使用機器學習來優化、加速核聚變的研究。
啥，機器學習+核聚變？是的，是不是很突破腦洞極限？
3、會議展示（Conference Demos）
谷歌在NIPS上將有兩場會議展示：
1）電子屏保具有高效、強健的移動視覺
Electronic Screen Protector with Efficient and Robust Mobile VisionHee Jung Ryu, Florian Schroff
在手機上通過人臉進行身份驗證，探索的也有一段時間了。但是如何在有很多人的擁擠空間中確定哪張臉是你的呢？
谷歌將在Demos中展示他們開發的DetectGazeNet，識別你只需47ms。
2）Magenta和deeplearn.js：實時控制瀏覽器中的深度生成音樂模型
Magenta and deeplearn.js: Real-time Control of DeepGenerative Music Models in the BrowserCurtis Hawthorne, Ian Simon, Adam Roberts, Jesse Engel, Daniel Smilkov, Nikhil Thorat, Douglas Eck
用深度學習來創作音樂的技術現在越來越成熟了，谷歌的團隊將展示如何在瀏覽器的javascript環境中運行deeplearn.js，從而讓用戶實時控制這些模型的生成。只需要一個瀏覽器，自己也能生產音樂，有沒有很高端？
4、workshops
所謂workshops，就是在某一主題下若干人一起進行密集討論的小會。NIPS 2017在8、9號兩天一共安排了53個Workshops。谷歌將參加其中的28個。
那麼這和自己有什麼關係呢？只能說，谷歌的眾多大神將在這些workshops閃亮登場，其中就包括那位女神（微笑）。來，看看都認識哪些人……
6th Workshop on Automated Knowledge Base Construction (AKBC) 2017Program Committee includes: Arvind NeelakantaAuthors include: Jiazhong Nie, Ni LaoActing and Interacting in the Real World: Challenges in Robot LearningInvited Speakers include: Pierre SermanetAdvances in Approximate Bayesian InferencePanel moderator: Matthew D. HoffmanConversational AI - Today's Practice and Tomorrow's PotentialInvited Speakers include: Matthew Henderson, Dilek Hakkani-TurOrganizers include: Larry HeckExtreme Classification: Multi-class and Multi-label Learning in Extremely Large Label SpacesInvited Speakers include: Ed Chi, Mehryar MohriLearning in the Presence of Strategic BehaviorInvited Speakers include: Mehryar MohriPresenters include: Andres Munoz Medina, Sebastien Lahaie, Sergei Vassilvitskii, Balasubramanian SivanLearning on Distributions, Functions, Graphs and GroupsInvited speakers include: Corinna CortesMachine DeceptionOrganizers include: Ian GoodfellowInvited Speakers include: Jacob Buckman, Aurko Roy, Colin Raffel, Ian GoodfellowMachine Learning and Computer SecurityInvited Speakers include: Ian GoodfellowOrganizers include: Nicolas PapernotAuthors include: Jacob Buckman, Aurko Roy, Colin Raffel, Ian GoodfellowMachine Learning for Creativity and DesignKeynote Speakers include: Ian GoodfellowOrganizers include: Doug Eck, David HaMachine Learning for Audio Signal Processing (ML4Audio)Authors include: Aren Jansen, Manoj Plakal, Dan Ellis, Shawn Hershey, Channing Moore, Rif A. Saurous, Yuxuan Wang, RJ Skerry-Ryan, Ying Xiao, Daisy Stanton, Joel Shor, Eric Batternberg, Rob ClarkMachine Learning for Health (ML4H)Organizers include: Jasper Snoek, Alex WiltschkoKeynote: Fei-Fei LiNIPS Time Series Workshop 2017Organizers include: Vitaly KuznetsovAuthors include: Brendan JouOPT 2017: Optimization for Machine LearningOrganizers include: Sashank ReddiML Systems WorkshopInvited Speakers include: Rajat Monga, Alexander Mordvintsev, Chris Olah, Jeff DeanAuthors include: Alex Beutel, Tim Kraska, Ed H. Chi, D. Scully, Michael TerryAligned Artificial IntelligenceInvited Speakers include: Ian GoodfellowBayesian Deep LearningOrganizers include: Kevin MurphyInvited speakers include: Nal Kalchbrenner, Matthew D. HoffmanBigNeuro 2017Invited speakers include: Viren JainCognitively Informed Artificial Intelligence: Insights From Natural IntelligenceAuthors include: Jiazhong Nie, Ni LaoDeep Learning At Supercomputer ScaleOrganizers include: Erich Elsen, Zak Stone, Brennan Saeta, Danijar HaffnerDeep Learning: Bridging Theory and PracticeInvited Speakers include: Ian GoodfellowInterpreting, Explaining and Visualizing Deep LearningInvited Speakers include: Been Kim, Honglak LeeAuthors include: Pieter Kinderman, Sara Hooker, Dumitru Erhan, Been KimLearning Disentangled Features: from Perception to ControlOrganizers include: Honglak LeeAuthors include: Jasmine Hsu, Arkanath Pathak, Abhinav Gupta, James Davidson, Honglak LeeLearning with Limited Labeled Data: Weak Supervision and BeyondInvited Speakers include: Ian GoodfellowMachine Learning on the Phone and other Consumer DevicesInvited Speakers include: Rajat MongaOrganizers include: Hrishikesh AradhyeAuthors include: Suyog Gupta, Sujith RaviOptimal Transport and Machine LearningOrganizers include: Olivier BousquetThe future of gradient-based machine learning software & techniquesOrganizers include: Alex Wiltschko, Bart van MerrinboerWorkshop on Meta-LearningOrganizers include: Hugo LarochellePanelists include: Samy BengioAuthors include: Aliaksei Severyn, Sascha Rothe
5、座談會（Symposiums）
NIPS 2017座談會共4場（12月7日），其中3場有谷歌大牛參與。
1）深化強化學習研討會
Deep Reinforcement Learning SymposiumAuthors include: Benjamin Eysenbach, Shane Gu, Julian Ibarz, Sergey Levine
2）可解釋的機器學習
Interpretable Machine LearningAuthors include: Minmin Chen
3）元學習
MetalearningOrganizers include: Quoc V Le
可以說，其中的每一個都是機器學習領域中深之又深的問題。諸位大神們對此的見解或許能刷新自己對機器學習的認識。
哦，對了，另外一場座談會是：智力的種類 - 類型、測試和滿足社會的需求（Kinds Of Intelligence: Types, Tests and Meeting The Needs of Society）
6、比賽（Competitions）
1）對抗攻擊防禦
Adversarial Attacks and DefencesOrganizers include: Alexey Kurakin, Ian Goodfellow, Samy Bengio
2）IV競爭：分類臨床可操作的基因突變
Competition IV: Classifying Clinically Actionable Genetic MutationsOrganizers include: Wendy Kan
7、研討會（Tutorial）
NIPS 2017共有9場研討會，谷歌只參加了其中之一：機器學習中的公平性（Fairness in Machine Learning）
Fairness in Machine LearningSolon Barocas, Moritz Hardt
二、有哪些大牛
Samy Bengio
谷歌大腦的研究科學家Samy Bengio是這屆大會的程序委員會主席（Program Chair），同時也將參加元學習的研討會（Workshop on Meta-Learning）以及組織「敵對攻擊和防禦」（Adversarial Attacks and Defences）的比賽。
Workshop on Meta-LearningPanelists include: Samy BengioCompetitionsAdversarial Attacks and DefencesOrganizers include: Alexey Kurakin, Ian Goodfellow, Samy Bengio
Ian Goodfellow
Ian Goodfellow是本屆大會的領域主席。由他組織了「機器欺騙」（Machine Deception）的研討會，此外他還將在一系列研討會中做特邀報告/keynote 報告：
Machine DeceptionOrganizers: Ian GoodfellowInvited Speakers include: Ian GoodfellowMachine Learning for Creativity and DesignKeynote Speakers include: Ian GoodfellowMachine Learning and Computer SecurityInvited Speakers include: Ian GoodfellowAligned Artificial IntelligenceInvited Speakers include: Ian GoodfellowDeep Learning: Bridging Theory and PracticeInvited Speakers include: Ian GoodfellowLearning with Limited Labeled Data: Weak Supervision and BeyondInvited Speakers include: Ian Goodfellow
除此之外，他還將和Samy Bengio、Alexey Kurakin等人共同組織「對抗攻擊防禦」（Adversarial Attacks and Defences）的比賽，這個比賽也是Ian Goodfellow所力推的。
Fei-Fei Li
作為國內諸多研究學子心目中的女神，李飛飛在NIPS上的活動相比於前面兩位大神則顯得有點少，她將出現在8日的這個研討會中：
Machine Learning for Health (ML4H)Organizers include: Jasper Snoek, Alex WiltschkoKeynote: Fei-Fei Li
記著，中午12點整開講。
Geoffrey E Hinton
Hinton在本次大會上甚至比李飛飛還要低調——只有入選的一篇論文，就是那個火爆一時的《Dynamic Routing Between Capsules》。然而，這篇論文甚至連oral都不是，只有一個5分鐘的spotlight。
Dynamic Routing Between CapsulesSara Sabour, Nicholas Frosst, Geoffrey E Hinton
注意了，5日下午4: 20-6: 00，Hall A。為了聆聽膠囊理論，估計這個會廳會擠爆頭！
去，要儘早！